{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RAPIDS + AtacWorks Visualization of Single-cell Chromatin Accessibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2020, NVIDIA CORPORATION.\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\") you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
    "\n",
    "http://www.apache.org/licenses/LICENSE-2.0 \n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we use here was made public by 10X Genomics. It contains single-cell ATAC-seq data from approximately 5000 human PBMCs. We will use the peak x cell matrix for clustering the population and the fragment file to calculate and visualize chromatin accessibility in each cluster. \n",
    "\n",
    "In this demonstration, we first cluster the PBMC cells similarly to previous examples. Next, we use cuDF to calculate and visualize chromatin accessibility in selected marker regions for multiple clusters. Finally, we use AtacWorks, a deep learning model to improve the accuracy of the chromatin accessibility track and call peaks in individual clusters. We show how AtacWorks can be used to characterize rare populations of cells and identify cell-type specific peaks.\n",
    "\n",
    "The code for AtacWorks is available [here](https://github.com/clara-parabricks/AtacWorks) and the method is described in detail in [this paper](https://www.nature.com/articles/s41467-021-21765-5). AtacWorks is available to run only on NVIDIA GPUs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import anndata\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import time\n",
    "import os, wget\n",
    "\n",
    "import cudf\n",
    "import cupy as cp\n",
    "from cuml.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import coverage\n",
    "import utils\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', 'Expected ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rmm\n",
    "rmm.reinitialize(\n",
    "   managed_memory=True, # Allows oversubscription\n",
    "   devices=0, # GPU device IDs to register. By default registers only GPU 0.\n",
    ")\n",
    "cp.cuda.set_allocator(rmm.rmm_cupy_allocator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scATAC data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We download a single-cell ATAC-seq dataset of 5000 PBMCs from the 10X genomics website. Specifically, we download the fragment file (for generating coverage tracks) and the peak x cell matrix (for clustering)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "FRAGMENT_FILE = '../data/5k_pbmcs_10X_fragments.tsv.gz'\n",
    "FRAGMENT_INDEX_FILE = '../data/5k_pbmcs_10X_fragments.tsv.gz.tbi'\n",
    "MATRIX_FILE = '../data/5k_pbmcs_10X.sparse.h5ad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading Peak x cell matrix...\n",
      "100% [......................................................................] 269198768 / 269198768Downloading fragment file...\n",
      "100% [............................................................................] 747629 / 747629CPU times: user 14.2 s, sys: 4.6 s, total: 18.8 s\n",
      "Wall time: 36.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "if not os.path.exists(MATRIX_FILE):\n",
    "    print('Downloading Peak x cell matrix...')\n",
    "    os.makedirs('../data', exist_ok=True)\n",
    "    wget.download('https://rapids-single-cell-examples.s3.us-east-2.amazonaws.com/5k_pbmcs_10X.sparse.h5ad', MATRIX_FILE)\n",
    "\n",
    "if not os.path.exists(FRAGMENT_FILE):\n",
    "    print('Downloading fragment file...')\n",
    "    wget.download('https://rapids-single-cell-examples.s3.us-east-2.amazonaws.com/5k_pbmcs_10X_fragments.tsv.gz', FRAGMENT_FILE)\n",
    "    wget.download('https://rapids-single-cell-examples.s3.us-east-2.amazonaws.com/5k_pbmcs_10X_fragments.tsv.gz.tbi', FRAGMENT_INDEX_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AtacWorks trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also download a trained AtacWorks model. This model is trained using low-coverage ATAC-seq data with a sequencing depth of ~5 million reads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ATACWORKS_MODEL_FILE = '../models/model.pth.tar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 245 µs, sys: 132 µs, total: 377 µs\n",
      "Wall time: 728 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "if not os.path.exists(ATACWORKS_MODEL_FILE):\n",
    "    print('Downloading AtacWorks model weights...')\n",
    "    os.makedirs('../models', exist_ok=True)\n",
    "    wget.download('https://api.ngc.nvidia.com/v2/models/nvidia/atac_bulk_lowcov_5m_50m/versions/0.3/files/models/model.pth.tar', ATACWORKS_MODEL_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set parameters for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of peaks to retain\n",
    "n_top_peaks = 20000\n",
    "\n",
    "# PCA\n",
    "n_components = 50\n",
    "\n",
    "# KNN\n",
    "n_neighbors=25\n",
    "knn_n_pcs=50\n",
    "\n",
    "# UMAP\n",
    "umap_min_dist=0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set AtacWorks parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Index of GPU to use for AtacWorks model\n",
    "gpu = 0\n",
    "\n",
    "# Parameters for use of the AtacWorks model\n",
    "interval_size = 50000\n",
    "pad = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load count matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 106 ms, sys: 190 ms, total: 296 ms\n",
      "Wall time: 421 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4654 × 84626"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "adata = sc.read(MATRIX_FILE)\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on these preprocessing steps, please refer to Example 4: Droplet single-cell ATAC-seq of 60K bone marrow cells from Lareau et al. 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessing_start = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tf-idf normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.16 s, sys: 619 ms, total: 1.77 s\n",
      "Wall time: 1.77 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lognormalized = utils.logtf_idf(adata.X, pseudocount=10**3)\n",
    "adata.X = lognormalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency-based peak selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4654, 20000)\n",
      "CPU times: user 455 ms, sys: 143 ms, total: 599 ms\n",
      "Wall time: 597 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "adata = utils.filter_peaks(adata, n_top_peaks)\n",
    "print(adata.X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing time: 2.39sec\n"
     ]
    }
   ],
   "source": [
    "print(\"Preprocessing time: %.2fsec\" % (time.time() - preprocessing_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster & Visualize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.5 s, sys: 7.03 s, total: 18.5 s\n",
      "Wall time: 18.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4654, 50)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "adata = anndata.AnnData(X=adata.X.todense(), obs=adata.obs,var=adata.var)\n",
    "adata.obsm[\"X_pca\"] = PCA(n_components=n_components).fit_transform(adata.X)\n",
    "adata.obsm[\"X_pca\"].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing time: 20.90sec\n"
     ]
    }
   ],
   "source": [
    "print(\"Preprocessing time: %.2fsec\" % (time.time() - preprocessing_start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 11.4 s, sys: 6.75 s, total: 18.1 s\n",
      "Wall time: 3.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sc.pp.neighbors(adata, n_neighbors=n_neighbors, n_pcs=knn_n_pcs, method='rapids')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: .obsp[\"connectivities\"] have not been computed using umap\n",
      "CPU times: user 184 ms, sys: 248 ms, total: 432 ms\n",
      "Wall time: 431 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sc.tl.umap(adata, min_dist=umap_min_dist, method='rapids')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Louvain clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 672 ms, sys: 330 ms, total: 1 s\n",
      "Wall time: 1.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sc.tl.louvain(adata, flavor='rapids')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(adata, color=['louvain'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can view the cluster assignment for each barcode:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.61 ms, sys: 316 µs, total: 4.93 ms\n",
      "Wall time: 4.2 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAACGAAAGCGCAATG-1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAACGAAAGGGTATCG-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAACGAAAGTAACATG-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAACGAAAGTTACACC-1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAACGAACAGAGATGC-1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cell cluster\n",
       "0  AAACGAAAGCGCAATG-1       5\n",
       "1  AAACGAAAGGGTATCG-1       3\n",
       "2  AAACGAAAGTAACATG-1       1\n",
       "3  AAACGAAAGTTACACC-1      10\n",
       "4  AAACGAACAGAGATGC-1       3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "clusters = pd.Series(adata.obs['louvain']).reset_index()\n",
    "clusters.columns = ['cell', 'cluster']\n",
    "cluster_ids = np.unique(clusters.cluster)\n",
    "clusters.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read fragments covering specified region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demonstration, we select a 100-kb region (including part of the MS4A1 marker gene) on chromosome 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "chrom = 'chr11'\n",
    "start = 60170000\n",
    "end = 60270000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use tabix to read all fragments overlapping this region from the fragments file. In order to match fragments to clusters, we merge the dataframe containing fragments with the cluster assignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2140 fragments were read from ../data/5k_pbmcs_10X_fragments.tsv.gz.\n",
      "CPU times: user 349 ms, sys: 75.1 ms, total: 424 ms\n",
      "Wall time: 665 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>cell</th>\n",
       "      <th>row_num</th>\n",
       "      <th>len</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr11</td>\n",
       "      <td>60214195</td>\n",
       "      <td>60214266</td>\n",
       "      <td>TGTAGCAGTACTAGAA-1</td>\n",
       "      <td>596</td>\n",
       "      <td>71</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr11</td>\n",
       "      <td>60266237</td>\n",
       "      <td>60266492</td>\n",
       "      <td>TGTGACAAGTAGAAGG-1</td>\n",
       "      <td>2014</td>\n",
       "      <td>255</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr11</td>\n",
       "      <td>60266468</td>\n",
       "      <td>60266506</td>\n",
       "      <td>TGTGACAGTCATGAGG-1</td>\n",
       "      <td>2058</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr11</td>\n",
       "      <td>60181444</td>\n",
       "      <td>60181855</td>\n",
       "      <td>TGTGACAGTGGACAGT-1</td>\n",
       "      <td>116</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr11</td>\n",
       "      <td>60250065</td>\n",
       "      <td>60250418</td>\n",
       "      <td>TGTGGCGGTAGGTGAC-1</td>\n",
       "      <td>1636</td>\n",
       "      <td>353</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   chrom     start       end                cell  row_num  len cluster\n",
       "0  chr11  60214195  60214266  TGTAGCAGTACTAGAA-1      596   71       9\n",
       "1  chr11  60266237  60266492  TGTGACAAGTAGAAGG-1     2014  255       4\n",
       "2  chr11  60266468  60266506  TGTGACAGTCATGAGG-1     2058   38      10\n",
       "3  chr11  60181444  60181855  TGTGACAGTGGACAGT-1      116  411       1\n",
       "4  chr11  60250065  60250418  TGTGGCGGTAGGTGAC-1     1636  353       3"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "fragments = coverage.read_fragments(chrom, start, end, FRAGMENT_FILE)\n",
    "print(str(len(fragments)) + ' fragments were read from ' + FRAGMENT_FILE + '.')\n",
    "fragments = fragments.merge(cudf.DataFrame(clusters), on=['cell'])\n",
    "fragments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate per-base sequencing coverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use cuDF to count the number of fragments overlapping each base in our selected region, in each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11, 100000)\n",
      "CPU times: user 2.65 s, sys: 106 ms, total: 2.76 s\n",
      "Wall time: 3.41 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "region_coverage = coverage.get_coverages(start, end, fragments)\n",
    "print(region_coverage.shape)\n",
    "region_coverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot per-base sequencing coverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we visualize this unsmoothed measure of chromatin accessibility across our selected region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x1000 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols = 3, nrows = 4, figsize = (16, 10), dpi=100)\n",
    "axs = axs.flatten()\n",
    "coords = np.arange(start, end)\n",
    "\n",
    "for (i, cluster) in enumerate(cluster_ids):\n",
    "    axs[i].plot(coords, region_coverage[i,:])\n",
    "    axs[i].set_title(f'Cluster {cluster}, Coverage')\n",
    "    axs[i].set_xlabel('Position (bp)')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Denoising and peak calling in selected clusters using AtacWorks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "atacworks_start_time = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count fragments per cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we count the number of fragments per barcode. Then, we match these to the cluster assignments of the respective barcodes in order to sum the total number of fragments for all barcodes within each cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56.7 s, sys: 2.03 s, total: 58.8 s\n",
      "Wall time: 59.2 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell</th>\n",
       "      <th>fragments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GTGCACGGTGTTGTTG-1</td>\n",
       "      <td>382101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AACTGGTAGCTCCGGT-1</td>\n",
       "      <td>339624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACTGCAAGTGCTTACA-1</td>\n",
       "      <td>328598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GCACCTTTCCAACGCG-1</td>\n",
       "      <td>322845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CCGTGAGAGACCATAA-1</td>\n",
       "      <td>319897</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cell  fragments\n",
       "0  GTGCACGGTGTTGTTG-1     382101\n",
       "1  AACTGGTAGCTCCGGT-1     339624\n",
       "2  ACTGCAAGTGCTTACA-1     328598\n",
       "3  GCACCTTTCCAACGCG-1     322845\n",
       "4  CCGTGAGAGACCATAA-1     319897"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "barcode_counts = coverage.count_fragments(FRAGMENT_FILE)\n",
    "barcode_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 228 ms, sys: 11.7 ms, total: 240 ms\n",
      "Wall time: 238 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fragments</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4417455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13095376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3133617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10924750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1862315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3549175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10223848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4782367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8741108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2351825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4536629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fragments\n",
       "cluster           \n",
       "0          4417455\n",
       "1         13095376\n",
       "2          3133617\n",
       "3         10924750\n",
       "4          1862315\n",
       "5          3549175\n",
       "6         10223848\n",
       "7          4782367\n",
       "8          8741108\n",
       "9          2351825\n",
       "10         4536629"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "cluster_counts = clusters.merge(barcode_counts, on='cell').groupby('cluster').sum()\n",
    "cluster_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select clusters for AtacWorks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this demonstration, we will select two clusters with close to 5 million reads. We will denoise the coverage track and call peaks for these clusters using the AtacWorks model trained on 5 million reads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['7', '10']\n",
      "CPU times: user 1.41 ms, sys: 0 ns, total: 1.41 ms\n",
      "Wall time: 1.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "num_clusters_to_denoise = 2\n",
    "clusters_to_denoise = abs(cluster_counts.fragments - 5000000).sort_values().iloc[:num_clusters_to_denoise].index.to_list()\n",
    "print(clusters_to_denoise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 100000)\n",
      "CPU times: user 1.25 ms, sys: 386 µs, total: 1.64 ms\n",
      "Wall time: 1.36 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "noisy_coverage = region_coverage[np.isin(cluster_ids, clusters_to_denoise)]\n",
    "print(noisy_coverage.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_coverage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load AtacWorks model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mLoading model weights from ../models/model.pth.tar...\u001b[0m\n",
      "\u001b[33mFinished loading.\u001b[0m\n",
      "CPU times: user 1.76 s, sys: 917 ms, total: 2.68 s\n",
      "Wall time: 12.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = coverage.load_atacworks_model(ATACWORKS_MODEL_FILE, gpu, interval_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference using trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 131 ms, sys: 0 ns, total: 131 ms\n",
      "Wall time: 41.2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "atacworks_results = coverage.atacworks_denoise(noisy_coverage, model, gpu, interval_size)\n",
    "track_pred = atacworks_results[:, :, 0].reshape(num_clusters_to_denoise, (end-start))\n",
    "peak_pred = atacworks_results[:, :, 1].reshape(num_clusters_to_denoise, (end-start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AtacWorks time: 72.34sec\n"
     ]
    }
   ],
   "source": [
    "print(\"AtacWorks time: %.2fsec\" % (time.time() - atacworks_start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 775 ms, sys: 196 ms, total: 971 ms\n",
      "Wall time: 674 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "fig, axs = plt.subplots(ncols = 2, nrows = 3, figsize = (16, 8), dpi=100)\n",
    "\n",
    "coords = np.arange(start, end)\n",
    "\n",
    "for (i, cluster) in enumerate(clusters_to_denoise):\n",
    "    axs[0][i].plot(coords, noisy_coverage[i])\n",
    "    axs[0][i].set_title(f'Cluster {cluster}, Noisy coverage')\n",
    "    axs[0][i].set_xlabel('Position (bp)')\n",
    "    \n",
    "    axs[1][i].plot(coords, track_pred[i], color='C1')\n",
    "    axs[1][i].set_title(f'Cluster {cluster}, Denoised coverage')\n",
    "    axs[1][i].set_xlabel('Position (bp)')\n",
    "    \n",
    "    axs[2][i].plot(coords, peak_pred[i] > 0.5, color='C2')\n",
    "    axs[2][i].set_title(f'Cluster {cluster}, Predicted peaks')\n",
    "    axs[2][i].set_xlabel('Position (bp)')\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full time: 105.47sec\n"
     ]
    }
   ],
   "source": [
    "print(\"Full time: %.2fsec\" % (time.time() - start_time))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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